GEN AI Instructor

Generative AI Instructor We are seeking an experienced and passionateGenerative AI Instructorto educate and empower aspiring developers, data scientists, and business professionals in the rapidly evolving field of Generative AI (GenAI). The ideal candidate will have 1-3 years of technical experience inMachine Learning (ML) , with significanthands-on experiencein developing, training, and deploying generative models. This role is critical in bridging the gap between foundational AI/ML knowledge and practical, real-world Generative AI application development.Roles & Responsibilities Conduct Structured Training:Design and lead comprehensive training programs on the theory, implementation, and application of Generative AI technologies, includingLarge Language Models (LLMs) ,Generative Adversarial Networks (GANs) , andDiffusion Models . Curriculum Development:Create and update high-quality learning materials, hands-on labs, guided projects, assignments, and assessments to ensure a cutting-edge and effective learning experience. Core Concept Instruction:Teach fundamental concepts such asdeep learning architectures(Transformers, VAEs),prompt engineering ,model fine-tuning (e.g., LoRA) ,transfer learning , andethical AIprinciples. Project Mentorship:Guide learners in building practical GenAI applications across various modalities, such astext generation (chatbots, summarization) ,image/video synthesis , andcode generation . Technical Support & Review:Review learner code, troubleshoot complex technical implementations, and provide one-on-one technical mentorship. Stay Current:Maintain deep expertise in the latest GenAI research, open-source models, frameworks, and cloud services. Facilitate Interactive Learning:Host live coding sessions, practical workshops, and in-depth Q&A/debugging sessions. Adapt Pedagogy:Adjust teaching methodology and content based on the learners' technical background, ranging from foundational Python/ML knowledge to advanced Generative AI development.Technology-Specific Responsibilities Generative Model Theory & Implementation: Teach the underlying principles ofLLMs, GANs, and VAEs/Diffusion Models . Guide implementation of generative models using frameworks likePyTorchorTensorFlow/Keras . Large Language Models (LLMs) & APIs: Instruct on working withcommercial APIs(OpenAI, Gemini, Anthropic) andopen-source models(Hugging Face ecosystem). Cover techniques forprompt optimization, system-level instructions, function calling/tool use , andmanaging token usage/cost . Train on advanced techniques likeRetrieval-Augmented Generation (RAG)andparameter-efficient fine-tuning (PEFT/LoRA) . Multimodal Generative AI: Provide training on image generation, manipulation, and video synthesis using models likeStable Diffusionor equivalent, including concepts likeinpaintingandControlNet . Explore code generation and pair programming use cases with GenAI tools. Deployment & MLOps: Instruct on model serialization, versioning, and deployment strategies for generative models using platforms likeHugging Face Hub ,Docker , and cloud AI services. Emphasizecost-efficient scalingandreal-time inference . Responsible AI & Ethics: Dedicate modules to the ethical implications of GenAI, focusing onbias detection/mitigation ,content moderation ,safety alignment , anddata privacy/copyrightconsiderations.Requirements 1-3 years of professional experiencein Python programming and Machine Learning/Deep Learning. Demonstrable practical experiencein building, training, and deploying Generative AI models (LLMs, GANs, VAEs, or Diffusion Models). Expertise in at least one major deep learning framework ( PyTorchorTensorFlow/Keras ). Strong understanding ofLLM architectures (e.g., Transformer) ,embeddings , andvector databasesfor RAG. Exceptional written and verbal communication skills, with a proven ability to mentor and explain complex technical concepts to diverse audiences. Preferred Skills Experience with advanced LLM frameworks likeLangChain, LlamaIndex, or Haystack . Familiarity with cloud-based AI/ML platforms (AWS SageMaker, Google Vertex AI, Azure ML). Prior experience in a technical training, teaching, or mentorship role. Experience withMLOps toolsfor generative models (e.g., weights & biases, MLflow). Working knowledge of deploying AI applications usingFastAPI, Streamlit, or Gradio .